{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "95505d4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "92c82477",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./citeseer/centrality analysis.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1c52942c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='degree'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "label = 'n.degree'\n",
    "pd.DataFrame(df[label]).value_counts().plot(xlabel=label[2:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "75508a19",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='eigenvector'>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "label = 'n.eigenvector'\n",
    "pd.DataFrame(df[label]).plot(xlabel=label[2:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e19e6477",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='pagerank'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "label = 'n.pagerank'\n",
    "pd.DataFrame(df[label]).plot(xlabel=label[2:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c48d8d52",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='closeness'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "label = 'n.closeness'\n",
    "pd.DataFrame(df[label]).plot(xlabel=label[2:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7ad7b2fc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='betweenness'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "label = 'n.betweenness'\n",
    "pd.DataFrame(df[label]).plot(xlabel=label[2:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "82fd63e8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='pregel_auth'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "label = 'n.pregel_auth'\n",
    "pd.DataFrame(df[label]).plot(xlabel=label[2:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7ee69036",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='pregel_hub'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "label = 'n.pregel_hub'\n",
    "pd.DataFrame(df[label]).plot(xlabel=label[2:])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:root] *",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
